Connect a repo. We scan it, quote a fixed price against an estimated timeline, and once you pay, a real engineer is assigned to harden it for security, stability, performance and deployment — then hands it back as a pull request.
Prefer GitHub's own install flow? Install the app instead → A human still follows up manually before anything happens.
Models are exceptional at producing code that runs. They're inconsistent at producing code that survives being deployed, attacked, or scaled. That gap is what gets fixed — nothing more, nothing speculative.
The vulnerabilities models reintroduce constantly because training data is full of them.
Code that works on the happy path and breaks the moment reality disagrees with it.
Correct but naive — the kind of inefficiency that's invisible in a demo and expensive at scale.
The part no model has opinions on, because it was never in scope for the prompt.
Every step is visible before you commit to the next. Nothing happens to your code that you haven't priced and approved.
Scoped, read-only access to the repo you choose. Nothing else in your GitHub account is touched.
Tip Most AI builders (Replit, Lovable, v0, Bolt) default new projects to public. Before connecting anything, switch the repo to private in its GitHub settings, unless you have a specific reason to keep it open.
We count lines, then read signals a raw LOC count can't tell you: test coverage, dependency age, language mix, CI presence.
Estimated days × day-rate, adjusted by the complexity profile — not raw LOC alone. You see the number before you pay it.
Payment starts the clock. A specific engineer — not a pool, not an AI agent — is assigned to your repo and branch.
No report lands in your inbox. A pull request does — every change explained inline, ready for you to review and merge.
If your build lives somewhere else, an export from a no-code tool, a local folder, a zip you've been passing around, we'll help you get it into a private GitHub repo first, at no charge, so the audit can start properly.
Most audits end in a PDF that tells you what's wrong and leaves the fixing to you — or to the same AI that caused the problem. We skip the document and do the work: the pull request itself is the record of what changed and why.
A findings list without a fix is a to-do list you didn't have time to write yourself. If a compliance trail is what you actually need, the PR description and commit history serve that purpose — reviewable, timestamped, attributable to a named engineer.
orders/query.py:88, closing an injection path..env.example committed to history.GET /invoices — 340ms → 22ms p50.LOC-based estimates are directional, not perfect. Here's exactly what happens when the engineer finds more than the quote priced for.
The assigned engineer flags scope overage as soon as it's found — typically within the first day of work, not at delivery.
A specific revised day count and price is sent. Work past the original quote doesn't start until you approve it.
Everything completed up to that point is still delivered as a PR — partial, clearly marked, yours regardless.
The same engine that quoted your live estimate above sets the real price — nothing changes between the demo and checkout.
Prefer GitHub's own install flow? Install the app instead → A human still follows up manually before anything happens.
We ask for access before we've earned your trust the slow way. These are the controls that stand in for it.
The GitHub App requests read access for the scan and write access scoped to a single working branch for delivery — never your default branch, never org-wide.
Every engagement is assigned to one identified engineer under NDA, not a rotating pool or an unreviewed contractor marketplace.
Your repository is never used to train a model, ours or anyone else's, and scan data is deleted on a fixed retention schedule after delivery.
Because a list of problems you still have to fix yourself isn't the outcome you're paying for. The pull request is the report — every change is explained inline, and the diff itself is the evidence of what was found and resolved.
You review it like any PR — comment, request changes, or decline to merge. Payment covers the engineer's time and the fix attempt, not a guarantee of merge; in practice, revision requests are handled within the same engagement at no extra charge.
Hardening what exists — pipeline config, secrets handling, rollback safety, observability wiring. Standing up CI/CD from nothing is treated as new scope and quoted as a change order, not assumed into the base price.
Engineers are matched to your stack at assignment. If the scan detects a stack we don't currently cover, you're told before payment, not after.
One engagement, one PR, by default. An ongoing retainer to keep a repo hardened as it grows is available separately once the first engagement closes.
No charge for the scan or the estimate — only for the work, and only once you approve the price.
Connect your code →Prefer GitHub's own install flow? Install the app instead → A human still follows up manually before anything happens.